噪声与振动控制2017，Vol.37Issue(4)：166-170,179,6.DOI:10.3969/j.issn.1006-1355.2017.04.032
Xgboost在滚动轴承故障诊断中的应用
Application of Xgboost to Fault Diagnosis of Rolling Bearings
摘要
Abstract
Due to the difficulty for selecting the data feature for different bearings and the low accuracy in fault diagnosis of rolling bearings using single classifier method, a rolling bearing fault diagnosis algorithm with eXtreme Gradient Boosting (Xgboost) based on classification and regression tree is proposed. The Xgboost is an ensemble learning method which contains a variety of classifiers. The accuracy of rolling bearing fault diagnosis is improved by the"boosting"thought of the Xgboost. First of all, the time domain statistical indicators extracted from the vibration signals of the rolling bearings are used as feature vectors. Then, the Xgboost algorithm is utilized for the fault diagnosis of the rolling bearings. Comparing the vibration data obtained in the bearing test on the SQI-MFS testing platform with the diagnostic results of traditional algorithms (SVM, kNN and ANN) and single classification and regression tree, it is concluded that the Xgboost algorithm is superior to the above algorithms, and the computation time is less than that of the traditional boosting decision tree algorithm.关键词
振动与波/滚动轴承/故障诊断/支持向量机/XgboostKey words
vibration and wave/rolling bearing/fault diagnosis/SVM/Xgboost分类
机械制造引用本文复制引用
张钰,陈珺,王晓峰,刘飞..Xgboost在滚动轴承故障诊断中的应用[J].噪声与振动控制,2017,37(4):166-170,179,6.基金项目
国家自然科学基金资助项目(NSFC 61403167) （NSFC 61403167）
